i
AN ABSTRACT OF THE THESIS OF
Callia K. Palioca for the degree of Honors Baccalaureate of Science in Biology presented
on May 31st, 2013.
Title: The Crystal Structure of the Potential Drug Target Mycoplasma pneumoniae
Glycerol 3-Phosphate Oxidase.
Abstract approved:
____________________________________________________
P. Andrew Karplus
Mycoplasma pneumoniae is the primary cause of community-acquired pneumonias,
including what is commonly known as walking pneumonia. This disease affects people from
all demographics, but especially children and older adults. Outbreaks are a significant public
health concern and work to develop new pharmacological agents is currently being researched.
How M. pneumoniae causes disease is not fully understood, but studies have pointed to
hydrogen peroxide as a pathogenicity factor. It is produced as a byproduct of glycerol
metabolism by the enzyme glycerol 3-phosphate oxidase (GlpO). Using X-ray crystallography,
we determined the three-dimensional structure of this enzyme in order to elucidate its binding
properties and guide structure-based drug design efforts. Here, we report the crystallization of
M. pneumoniae GlpO along with the native structures of oxidized and reduced GlpO at
resolutions of to 2.4 Å and 2.5 Å, respectively. We compared GlpO from M. pneumoniae to
another GlpO, a glycerol 3-phosphate dehydrogenase, a glycine oxidase, and the most
structurally similar protein which is a protein of an unknown function from Bordetella
pertussis.
Key Words: Mycoplasma pneumoniae, X-ray crystallography, walking pneumonia, GlpO,
structure-based drug design
Corresponding email address: callia.palioca@gmail.com
ii
©Copyright by Callia K. Palioca
May 31st, 2013
All Rights Reserved
iii
The Crystal Structure of the Potential Drug Target
Mycoplasma pneumoniae Glycerol 3-Phosphate Oxidase
By
Callia K. Palioca
A PROJECT
Submitted to
Oregon State University
University Honors College
in partial fulfillment of
the requirements for the
degree of
Honors Baccalaureate of Science in Biology (Honors Scholar)
Presented May 31st, 2013
Commencement June 15th, 2013
iv
Honors Baccalaureate of Science in Biology project of Callia K. Palioca presented on
May 31st, 2013
APPROVED:
Mentor, representing Biochemistry and Biophysics
Committee Member, representing Biochemistry and Biophysics
Committee Member, representing Zoology
Chair, Department of Biology
Dean, University Honors College
I understand that my project will become part of the permanent collection of Oregon
State University, University Honors College. My signature below authorizes release of
my project to any reader upon request.
Callia K. Palioca, Author
v
ACKNOWLEDGMENTS
This thesis has been the culmination of 4 years of hard work but I would not be here
today without the help of so many people. To the following people, I thank you for all
that you have done for me and will never forget how much you have supported me.
A large thank you to Andy Karplus for teaching me about science and about the world.
You are highly accomplished yet dedicate your life to mentoring students. You believed
in me from the beginning and gave me my first real job. You taught me how to think like
a scientist and have been a huge support in editing this thesis. You never gave up on me
and have mentored with patience and compassion. I have enjoyed being a part of the lab
and I thank you for all the fun lab dinners we have had. I could not have asked for a
better thesis mentor!
To the Karplus lab, I never would have learned so much or gotten so far without your
guidance. Thank you for all the helpful discussions, the fun, and for being so willing to
help me. I would have been clueless without your help.
To Kevin Ahern and Indira Rajagopal for supporting me and advising me throughout my
years at OSU. You have been an inspiration from the start and I have enjoyed getting to
know you and learn many of life’s lessons from you. Thanks for the laughs and the
yummy food.
To Devon Quick for reading this very long thesis. Thank you for being such a wonderful
anatomy and physiology professor. I have learned so much from you that will help me in
my future career. Thank you for also being role model to me and fellow students.
To Ms. Balogh, for first showing me proteins. You did not just teach me about structure
and function, but you let me figure it out on my own. I will never forget the epiphany of
learning how hemoglobin binds oxygen, watching the structure change before my eyes.
Your effort and dedication to teaching are an inspiration and I am lucky to have had such
an effective and fun teacher like you.
To my parents for making me weed the yard and clean the dishes. You have taught me
how to be a hard worker and have been an absolute supporter of everything I do. Mom
you have been to every soccer game and Dad, I thank you for instilling a love of nature in
me. Thank you for your hard work, love and support.
To my fiancé, Issac for believing in me throughout everything. You have been there for
every hardship and struggle and helped me see the good even in the toughest times. You
have pushed me to do what I needed to do and never put up with my excuses. I look
forward to all the fun times we will have together. I love you very much!
vi
TABLE OF CONTENTS
Heading
Page
Chapter 1 INTRODUCTION……………………………………………………..….1
Overview of the Project…………………………………………………….....1
Mycobacteria as a Significant Cause of Human Suffering……………………1
M. pneumoniae and its Associated Diseases…………………………………..3
Symptoms and overall effects of M. pneumoniae infection on
the host………………………………………………………..3
Demographics and epidemiology of community-acquired
pneumonia……………………………………………………..4
Connections to other conditions……………………………………….5
Current strategies of detection…………………………………….......6
Current strategies of treatment………………………………….……..8
Proposed Mechanisms of Pathogenicity of M. pneumoniae…………………..9
Current models for how M. pneumoniae causes disease – Gliding
motility & attachment organelle……………………………...10
Current models for how M. pneumoniae causes disease – Production
of a cytotoxin………………………………………………...12
Current models for how M. pneumoniae causes disease – Production
of peroxide…………………………………………………...12
Glycerol Phosphate Oxidase (GlpO) as a Drug Target……………………….13
What is Currently Known about the Structure and Functional Mechanism
of GlpO………………………………………………………………15
Solving the Crystal Structure of M. pneumoniae GlpO……………………....16
Chapter 2 MATERIALS AND METHODS………………………………………….17
Brief Overview of X-ray Crystallography Experiments in this Work………...18
Cloning and Protein Purification………………………………………………22
Crystallization, Data Collection, and Data Processing………………………...22
vii
TABLE OF CONTENTS (Continued)
Heading
Page
Structural Determination and Refinement of Oxidized MpGlpO……………..28
Molecular replacement………………………………………………...28
Manual modeling and refinement – stage 1:
refinement of GlpOox1 model…………………………………32
Manual modeling and refinement – stage 2:
addition of GlpOox2 data……………………………………...34
Manual modeling and refinement – stage 3:
final adjustments and metal chemistry…………………………36
Structural Comparisons………………………………………………………...39
Chapter 3 RESULTS AND DISCUSSION……………………………………………40
Enzymatic Activity and Oligomeric Structure in Solution…………………….40
Solution of MpGlpO Structure…………………………………………………41
Overall Structure……………………………………………………………….43
MpGlpO as a Member of the DAAO Superfamily…………………………….51
Active Site Characterization……………………………………………….......55
The Reduced MpGlpO Structure………………………………………………60
Conclusions and Outlook………………………………………………………60
BIBLIOGRAPHY……………………………………………………………………...63
viii
LIST OF FIGURES
Figure
Page
1. Morphology of M. pneumoniae…………………………………………….......11
2. Overview of M. pneumoniae Pathogenicity……………………………………14
3. Overview of X-ray Crystallography……………………………………………19
4. MpGlpO Cryo-protectant Optimization Images………………………………..27
5. Ala 22 Comparison and FAD Internal Control………………………………...33
6. Progress of Model Building and Refinement of R and Rfree…………………...37
7. An Alternate Backbone Conformation Example....…………………………….42
8. An Alternate Side Chain Conformation Example...……………………………44
9. Nickel Ion Coordination and Potential Dimer Interface……………………….45
10. Synchrotron Source Scan of MpGlpO aids Identification of the Metal Ion........46
11. The Topology of MpGlpO, SsGlpO, and Bp3DME……………………………47
12. Stereo Ribbon Diagram of MpGlpO……………………………………………50
13. Structure-based Sequence Alignment of MpGlpO and Similar Enzymes……...53
14. Overlay of Active Sites of MpGlpO, SsGlpO, and Bp3DME …………………56
15. Comparison of Flavin Bending…………………………………………….......57
16. Stereo Overlay of MpGlpO, SsGlpO, and Bp3DME…………………………...59
17. Reduced GlpO Difference Map………………………………………………...61
ix
LIST OF TABLES
Table
_Page
1. Data Collection and Refinement Statistics for GlpOox and GlpOred…………26
2. Overview of MpGlpO with Soaked Substrates and Results……………………29
3. Rounds of Refinement Table……………………………………………….......35
The Crystal Structure of the Potential Drug Target Mycoplasma pneumoniae
Glycerol 3-Phosphate Oxidase
CHAPTER 1
INTRODUCTION
Overview of the Project
Mycoplasma pneumoniae is the bacterium that causes primary atypical
pneumonia, otherwise known as walking pneumonia. A type of community-acquired
pneumonia (CAP), this condition is a worldwide health threat for vulnerable populations,
which include children and the elderly. Understanding the spread of disease and the
mechanisms of pathogenicity are essential to providing better treatment and prevention.
This introductory chapter largely provides background information about the genus
Mycoplasma, the diseases caused by M. pneumoniae, their impact, and theories into the
pathogenicity. The remainder of this thesis then describes the process of determining the
structure of a protein thought to be a main pathogenicity factor causing the symptoms of
walking pneumonia.
Mycoplasmas as a Significant Cause of Human Suffering
Mycoplasmas are a unique genus of bacteria which were first isolated in culture in
1898 as a bovine pleuropneumonia agent. Prior to that, they were described as a type of
fungus. Indeed, the name mycoplasma derives from mykes (fungus) and plasma
(formed).1 Interestingly, these organisms have the smallest cell and genome sizes of any
self-replicating, autonomous organisms.2 For comparison, the Escherichia coli genome is
five times larger than the 816 kb genome of one species from this genus, M.
pneumoniae.2,3 Many members of this genus have been the subject of research into
identifying the minimal number of genes required for cell existence. Current evidence
2
exists that these bacteria are continually undergoing reductive genome evolution.3
Mycoplasmas have also been extensively because of their unique lack of a cell wall.
Structurally, they are able to compensate for this lack of cell wall by having sterols in the
cell membrane that provide physical support. Functionally, they also compensate by
having a close adaptation with the eukaryotic host. The lack of a cell wall has also meant
that the cell is extremely susceptible to desiccation and therefore, close contact is
necessary for transmission by airborne droplets.2 Because all members of this genus lack
a cell wall and do not have the ability to synthesize peptidoglycan, they are unaffected by
typical antibiotics such as penicillin which work by targeting cell wall synthesis.
These bacteria play a role in diseases affecting humans, other mammals
(including cows and pigs), reptiles, fish, arthropods, and plants.4 One specific example is
arthritis in mice, rats, cattle, swine, and poultry. Many mycoplasma species cause
respiratory diseases. Pneumonias caused by mycoplasmas are found in humans, (M.
pneumoniae), calves (M. bovis), sheep and goats (M. capricolum), swine (M. hyorhinis),
and turkeys (M. gallisepticum). A variety of mycoplasmas, both harmless and pathogenic
can be isolated from the human body. In one study group, M. orale was isolated in 84%
of gingival crevices. M. hominis can be found in the human urogenital tract but when it
turns pathogenic, it causes 5% of cases of pyelonephritis (a urinary tract infection that has
reached the kidneys) and even causes some cases of pelvic inflammatory disease.5
One particularly well-studied organism, Mycoplasma mycoides, subsp. mycoides
SC, where SC stands for small colony and the organism is commonly called M. mycoides,
is responsible for causing contagious bovine pleuropneumonia (CBPP). As the most
common form of cattle disease in Africa, eradication of this virulent pathogen is of
3
ecological and economic importance.6 M. pneumoniae, the causative agent of walking
pneumonia, has similar proteins as those found in M. mycoides, including a sequencesimilar glycerol 3-phosphate oxidase thought to be pivotal in causing both CBPP and
atypical pneumonia. Eradicating the diseases caused by these organisms would enhance
the quality of life of many individuals, as well as save money by avoiding healthcare
costs of hospitalizations, the loss of cattle, and the loss of productive hours.
M. pneumoniae and its Associated Diseases
In 1944, Eaton et al.7 isolated and described what they called an ‘Eaton agent’
from the sputum of a patient with primary atypical pneumonia. This is a type of
pneumonia which does not respond to therapy with sulfonamides or penicillin.5 While it
was originally thought to be a virus, it was reclassified as a pleuropneumonia-like
organism in 1961 and given the taxonomic designation of Mycoplasma pneumoniae in
1963.2 Since then, it has been the subject of numerous studies and research articles. For
those interested in more in-depth information, this topic has been extensively reviewed
by Waites and Talkington.2
Symptoms and overall effects of M. pneumoniae infection on the host
M. pneumoniae infections can occur in the upper and lower respiratory tracts. The
severity and longevity of symptoms varies, but the symptoms typically consist of a sore
throat, hoarseness, sinus congestion, headache, middle ear infection, and a persistent,
hacking cough commonly associated with atypical pneumonia.2 Many patients also
experience flu-like symptoms, which differentiates these infections from typical
pneumonia.5
4
Local damage of the respiratory tract causes many of these symptoms. As an
example, the M. pneumoniae bacteria reach the bronchi and bronchioles causing
vacuolization and, in more severe cases, total destruction of bronchi cilia.8 It is thought
that these manifestations contribute the most to the hacking cough which subsequently
spreads the bacteria.2 Further, many cells involved in the immune response, such as
macrophages and neutrophils, can accumulate in the tissues. These can lead to lesions
that continue to harm the host, showing how the host immune system can exacerbate the
disease. However, many infected individuals never progress to the more severe stages of
this disease involving a lower respiratory infection, and up to 20% are asymptomatic.9
Demographics and epidemiology of community-acquired pneumonia
While M. pneumonia infection is sometimes regarded as moderately harmless
and more of an inconvenience, it has been known to cause hospitalizations, sometimes
affecting large parts of a community, loss of productivity, and even death. These atypical
pneumonias fall in the category of community-acquired pneumonias (CAPs), and in one
study, M. pneumoniae accounted for 20.7% of adult CAP cases,2 making it the single
most frequently identified pathogen for these conditions.
In general, atypical pneumonia affects the elderly and children. M. pneumoniae
was the second most common pathogen found in hospitalized adults with CAP-like
symptoms in a survey of two Ohio counties in 1991, where a significantly higher
percentage of people over the age of 65 were hospitalized in comparison to younger
adults.10 These bacteria were also recently shown to cause more than 100,000
hospitalizations in the U.S. each year.2 Children are also susceptible to infection. In
Finland, this bacterium was found in over 50% of children aged 5 years and older with
5
CAP.11 The rest of the population is not immune, though, as many parents acquire the
disease from their children.2 Fortunately, however, it can take many weeks for family
members to become infected, giving families time to begin treatment.5
In the United States, most outbreaks occur in late summer and early fall8, but they
can happen anywhere and at any time. Outbreaks often start in closed or semi-closed
areas such as schools, military bases, or hospitals.2 Based on serological studies done in
Denmark from 1946 to 199512, endemic disease transmission, or transmissions localized
in a small population, were interspersed with cyclic epidemics every 3-5 years. The long
incubation period, slowly progressing symptoms, and the ability of most patients to
continue daily activities (which gave rise to the colloquial term “walking pneumonia”)
may help explain the cyclical pattern of outbreaks.2
Connections to other conditions
Besides atypical pneumonias, M. pneumoniae has been implicated in chronic
asthma, encephalitis and rheumatoid arthritis. Since the 1980s, this organism has been
seen as a trigger for acute asthmatic attacks. Patients for whom asthma symptoms have
persisted for years, consistent with a stubborn infection, tend to have had M. pneumoniae
infections.8 In one study, patients saw significant improvement in symptoms after a 6week treatment trial of clarithromycin13, an antibiotic commonly used against this
bacterium.
In addition, M. pneumoniae was the most common infectious agent identified in
one study of 2000 people with encephalitis, an inflammation of the brain with associated
headaches and seizures in more extreme cases.8 Clusters of encephalitis conditions tend
to occur during outbreaks of M. pneumoniae. In one example, the death of an elementary
6
school student with encephalitis occurred during a mycoplasma outbreak in the local
area.14,15 Rheumatoid arthritis has similarly been linked M. pneumoniae in some studies.
Consistent with this association, rheumatoid arthritis can often be successfully treated by
tetracyclines, a group of drugs to which M. pneumoniae is also sensitive.16 Both this
condition and encephalitis have complex and still not fully understood etiologies. It has
been suggested that because the adhesins on the bacterial cells have similarities to host
structures, molecular mimicry (an autoimmune response based on similar antigens
present on both the bacterium and host cells,16,17) may be important in developing
inflammation that would be associated with both conditions.8,16
Current strategies of detection
Since the initial discovery of M pneumoniae, detection in a clinical setting has
proven to be inconsistent and difficult. Each of the available approaches such as culture,
serology, and Polymerase Chain Reaction (PCR) has inherent limitations. As such,
current diagnosis relies on a combination of PCR and serology. IgM serology has been
shown to be most useful in children while IgA serology (not yet universally available)
works best for older adults.2
Culturing has the unique advantage in that, if positive and done properly, the
identification is 100% specific to M. pneumoniae. However, M. pneumoniae is a
fastidious and slow-growing organism that can take up to 6 weeks to grow on
microbiological medium, an impractical amount of time for clinical diagnosis. Supplies
are also very expensive and technical expertise is required. As such, culturing has not
been utilized as an effective clinical diagnostic tool for M. pneumoniae.
7
Tests for M. pneumoniae using serology have been inconsistent in detecting M.
pneumoniae in patients. Serologic tests utilize serum, the liquid portion of blood after the
cells have been removed from whole blood. Serum contains antibodies, which are
essential for immune system function and also are markers that can indicate what type of
infection is present in the body. One type of antibody, IgM, indicates when there is an
acute infection, while IgG indicates a past infection or immunity. Tests are available that
detect each of these antibodies or both. IgA is another antibody class that is typically
produced early in an infection. Serology is inadequate in immunosuppressed patients
because it relies on the immune system to produce antibodies. Antibody production can
also be delayed in some people, and thus the date of serum collection can alter the result.
As such, serology is considered a very useful tool for epidemiological studies, but less so
for the diagnosis of individual patients.8
Since their development in the late 1980s, PCR assays have been essential tools of
the biological sciences. The sensitivity is high and only a single organism is needed to
make a diagnosis. It seems to be highly effective with identifying M. pneumoniae in
patients with extrapulmonary syndromes such as encephalitis. Nevertheless, a common
difficulty with identifying M. pneumoniae as the pathogen responsible for someone's
symptoms is the similarity it has with other pathogens such as C. pneumoniae and
Legionella pneumophila. Development and commercialization of PCR assays that can
differentiate between these microorganisms would be very beneficial in both research and
healthcare settings.
As stated previously, studies now point to the combined application of PCR
assays and serological tests for detecting M. pneumoniae in suspected cases of atypical
8
pneumonia. However, standardization and distribution of a cost-efficient, multiplex
detection technology is still needed.2
Current strategies of treatment
Due to the lack of a consistent and standardized method for detecting of M.
pneumoniae, evaluation of the efficacy of available drugs against this pathogen is
difficult. Generally, antibiotics are the primary treatment option. Macrolides are
considered the treatment of choice for atypical pneumonia.8 Erythromycin and
azithromycin are examples of this group of drugs. While use has been effective in
reducing symptoms in patients, there is concern over macrolide-resistant M. pneumoniae.
In 2001, researchers from Japan described and isolated a resistant strain from children
with pneumonia and bronchitis. Resistance to both azithromycin and erythromycin was
observed. Overall, it is estimated that 10-33% of isolates obtained between 2001 and
2006 were macrolide-resistant. The CDC reported that 27% of M. pneumoniae in an
outbreak in the northeastern region of the United States from 2006 to 2007 were
macrolide-resistant. In fact, the predominant resistant strain in the U.S. is the same as one
of those found in Japan.18 Patients with macrolide-resistant M. pneumoniae typically have
to be changed to a different type of drug group because of persistent fever, cough, and no
resolution of symptoms.8
The other two main types of antibiotics useful for atypical pneumonia are
tetracyclines and fluoroquinolones. However, the ability to effectively apply these drugs
is limited. In particular, tetracycline is not approved either for use in children under the
age of 8 or in pregnant women. Similarly, fluoroquinolones are not recommended for
children because their bones have not fully ossified. This antibiotic seems to cause
9
toxicity in cartilage.2 While natural isolates of M. pneumoniae bacteria have not been
found to be resistant to these drugs, in-vitro experiments have developed resistant
mutants.8
Due to antibiotic resistance, it would be beneficial to have a type of primary
prevention technique. The development of a vaccine against M. pneumoniae would be
appropriate for prevention because of its propensity for outbreaks in schools, hospitals,
and military bases. However, there has so far been little success in vaccine development.
In some cases, symptoms were exacerbated, with a trial vaccine sensitizing the host in
some way and resulting in a more severe illness after being exposed to M. pneumoniae.2
One method of combating M. pneumoniae would be to synthesize a drug that
specifically targets this pathogen. This can be done through structure-based drug
design.19 With the aid of X-ray crystallography, a three-dimensional structure of a protein
drug target can be determined. By selecting and optimizing molecules based on drugbinding properties and algorithms, a drug can be ‘tailor-made’ for a specific protein. The
very specific binding of the drug molecule can reduce side interactions with other
molecules, reducing patient side effects. Drugs may also be prescribed at lower dosages if
efficacy and specificity are increased.19 However, to effectively choose a lead molecule
and drug target, more information must be acquired about the mechanisms of
pathogenicity of M. pneumoniae.
Proposed Mechanisms of Pathogenicity of M. pneumoniae
Many processes come together for M. pneumoniae to cause virulence. The
bacteria must get to the proper spot next to the lung epithelium (using gliding motility),
10
adhere to the cells (via the attachment organelle), and produce toxins such as cytotoxins,
peroxides, or both. The precise mechanism and the relative influences of peroxide versus
cytotoxins are still not known. Current knowledge on these topics is extensively reviewed
by Waites and Talkington.2
Current models for how M. pneumoniae causes disease – Gliding motility & Attachment
For this pathogen to cause disease, it must first reach the target cell through a
process known as gliding motility. Most organisms move using flagella. However, M.
pneumoniae does not have flagella and instead glides across surfaces, never changing the
leading end.20 While the exact mechanisms are not understood, it is essential to cause
virulence. A mutant organism that lacks a protein involved in gliding motility, was shown
to reduce gliding velocity to 5% of that of the wild type and, in a separate study, the
patient infected by this mutant organism had reduced lung lesions when compared to a
patient infected with a wild type M. pneumonaie.21 Gliding motility relates to attachment
in that it uses the attachment organelle2, a structure seen in the image of M. pneumoniae
in Fig. 1.
Once the parasite is in the proper location of the respiratory tract of an individual,
M. pneumoniae must adhere to the epithelial lining. This adhesion step is also essential to
pathogenicity. Specific proteins that are concentrated on the tip of an attachment
organelle reach out from this extension to bind to host receptors for fibronectin, allowing
the bacterium to gain access to cell proteins and nutrients.2 Inhibition of the main protein
P1 by specific antibodies results in a 75% reduction of respiratory epithelium attachment
by virulent M. pneumoniae.22
11
Fig 1: Morphology of M. pneumoniae. This scanning electron micrograph shows that M.
pneumoniae has a thick body with two thinner ends. Arrows point to the attachment
organelle which is essential in adherence to host epithelium. This figure was adapted
from Fig. 4 of Waites and Talkington.2 Original credit assigned to Krause and TaylorRobinson.5
12
Current models for how M. pneumoniae causes disease – Production of a cytotoxin
Another virulence factor recently discovered is the CARDS TX, or communityacquired respiratory distress syndrome toxin.18 This protein, encoded within the M.
pneumoniae genome, has a 27% amino acid sequence identity to pertussis toxin in over
239 residues.23 Key similarities include NAD binding and ADP-ribosylating activity
residues seen in pertussis toxin. It is not yet clear whether these toxins act via the same
mechanism.23,24 A recombinant CARDS TX caused vacuolization of mice cells in a dosedependent fashion and caused reduced ciliary movement in baboon trachea23. Both mice
and baboon models treated with this toxin had inflammatory responses and reduced
airway function similar to those observed in M. pneumoniae infection.25 These studies
indicate that the CARDS TX can play a significant role in the pathogenesis of M.
pneumoniae. Studies are currently underway to determine the structure of this toxin.24
Current models for how M. pneumoniae causes disease – Production of peroxide
Generation and secretion of hydrogen peroxide have also been identified as key
factors of M. pneumoniae pathogenicity.3 The peroxide is primarily produced as
byproduct of glycerol metabolism from the enzyme α-glycerol 3-phosphate oxidase
(GlpO). As a reactive oxygen species (ROS), the peroxide can cause damage to the host’s
cells by inducing oxidative stress, a state of a greater number of oxidants than
antioxidants.26 If the peroxide is partially reduced, the radical hydroxyl ion produced can
react with lipids on the host plasma membranes, changing the shape of polyunsaturated
lipids.26 This can puncture the membrane and affect membrane fluidity, thus impacting
homeostasis of the cell. Peroxide can also enter the host cell and induce inflammatory
processes.6 The inflammation can, on the one hand, minimize disease by triggering host
13
defense mechanisms that eliminate the organism. However, on the other hand, the
peroxide induced expression of host pro-inflammatory genes can exacerbate disease
though damage of the respiratory epithelium and surrounding tissues.6,18 Although
humans have rather robust antioxidant enzymes, it appears that M. pneumoniae produces
superoxide anion that inhibits the host enzyme catalase, that normally protects it from
hydrogen peroxide.8, 27 This makes the host more susceptible to oxidative damage.
Glycerol Phosphate Oxidase (GlpO) as a Drug Target
For this project, we focused on blocking the production of peroxide as a way to
decrease M. pneumoniae pathogenicity. As noted above, the peroxide that causes
pathogenicity is a product of glycerol metabolism. The glycerol is derived from host
phospholipids and lung epithelia surfactant. As can be seen in Figure 2, glycerol is
brought into the bacterial cell with the help of a glycerol facilitator protein (GlpF). It is
then phosphorylated to glycerol 3-phosphate by glycerol kinase (GlpK), and finally
oxidized by GlpO (sometimes referred to as a GlpD based on naming conventions
formulated prior to its characterization as an oxidase). This final oxidation step forms
H2O2 as a byproduct. The mechanism of this process in the better studied virulent related
organism M. mycoides can give insight into the biochemical pathway for cytotoxicity of
M. pneumoniae. 3
In M. mycoides, H2O2 is formed via a GlpO. This enzyme oxidizes αglycerolphosphate (Glp) (also known as glycerol 3-phosphate) to dihydroxyacetone
phosphate (DHAP) by using a flavin adenine dinucleotide (FAD) molecule and oxygen to
produce hydrogen peroxide. This is in contrast to most glycerol metabolism mechanisms,
14
Figure 2: Overview of M. pneumoniae Pathogenicity. This extracellular organism
adheres to tracheal and lung epithelia. There it uptakes glycerol from host phospholipids,
bringing this molecule into its cell using the transporter GlpF. A phosphate is
enzymatically added by GlpK to give glycerol 3-phosphate (Glp). Carbon numbers are
indicated on this molecule. Glp acts as a substrate, along with O2, for GlpO which
produces both DHAP and toxic H2O2. DHAP is utilized in glycolysis to yield ATP for the
bacteria. Due to the bacterium’s adherence to the host, the peroxide is shuttled out of the
bacterial cell and contacts the epithelium, causing vacuolization and ciliary destruction.
These changes contribute to the symptoms of walking pneumonia.
15
which utilize a dehydrogenase enzyme that reduces NAD+ to NADH.3 As expected,
DHAP can then enter into glycolysis to produce ATP, which can be utilized for energy.
Based on sequence and protein structure similarities, it is hypothesized that M.
pneumoniae works by an identical mechanism. Due to the cytotoxic effects of peroxide,
M. pneumoniae GlpO (MpGlpO) is a potential target for structure-based drug design.
What is Currently Known about the Structure and Functional Mechanism of GlpO
While the three-dimensional structure was not known prior to this report, some
insights were drawn from previous studies on both the structure and the mechanism of
function. Based on searches that identify similar proteins, MpGlpO is part of the D-amino
acid oxidase (DAAO) family of flavoenzymes. These proteins use the flavin of an FAD
cofactor to carry out a two-step reaction. First, they oxidize carbon-nitrogen bonds of
amino acids, primary amines, or secondary amines while the enzyme bound FAD is
reduced to FADH2. Then, the reduced flavin is re-oxidized by O2 and this forms H2O2 as
a byproduct.28 The mechanism by which the flavin is reduced has been under debate
since the discovery of DAAO. Structural studies of DAAO at high resolution indicate
that a hydride transfer occurs from the substrate α-carbon to the reactive N5 of the
flavin.29 The structure showed that amino acid ligands were bound in the correct position
for a hydride transfer.29 Specifically, the α-hydrogen is pointed towards the flavin N5
atom, which is the site of reactivity. Because the flavin in DAAO enzymes reacts via a
hydride transfer, it is expected that the flavin in MpGlpO will react in a similar fashion,
and that it is the C2-atom of Glp (Fig. 2) that corresponds with the α-carbon of the
substrates of the related enzymes.30
16
Solving the Crystal Structure of M. pneumoniae GlpO
Having new, more effective drugs for treating M. pneumoniae infections and
disease would be beneficial for the public health, by providing more efficient forms of
treatment for walking pneumonia, an example of a community-acquired pneumonia that
afflicts hundreds of thousands of people every year. As described above, the production
of hydrogen peroxide by GlpO as a part of glycerol metabolism is a crucial contributor to
M. pneumoniae pathogenicity. Elucidation of the three-dimensional structure of GlpO is a
foundational step required to guide the structure-based development of new drugs, and
the focus of this thesis work is the successful use of X-ray crystallography to solve the M.
pneumoniae GlpO structure. In the next two chapters, I present the crystallization
techniques, the native, oxidized structure of GlpO to 2.4 Å resolution, and the reduced
form to 2.5 Å resolution. I also discuss the process by which we arrived at this structure,
as well as comparisons to similar structures.
17
CHAPTER 2
MATERIALS AND METHODS
A scientific article presents a condensed story of key experiments and
observations leading to logical conclusions that represent advances in knowledge.
However, the deeper story, that of the detours, the dead ends, and the strategies
implemented to try to navigate around problems, is less frequently discussed. In contrast
with typical Materials and Methods sections, which include brief technical descriptions
providing only necessary information needed for others to understand and repeat the
work done, in this section I will also describe some of the above-mentioned broader
meandering aspects of this project. These were a very impactful part of my experience in
scientific research. I make this attempt to reveal more of the entire story, because it was
through my experience of these meanderings that I now better understand how scientific
research really works. I hope that readers will find this presentation enjoyable, and that
they also will find it gives them a more accurate picture of the kinds of challenges faced
by scientists as they pursue a research project.
In this chapter, I will first provide a brief overview of the steps typically taken in
determining the structure of protein using X-ray crystallography. I will then describe my
process of taking a protein solution and growing a crystal, collecting data from that
crystal, and solving the structure and refining it to get a three-dimensional model of
MpGlpO.
18
Brief Overview of X-ray crystallography Experiments in this Work
Before delving into the details of my research, I provide some background on the
X-ray crystallography methods used to solve the structure of MpGlpO. X-ray
crystallography is, along with nuclear magnetic resonance (NMR), one of the two ways
to solve a high resolution protein structure. As outlined in a flow chart (Figure 3), in this
method, a protein crystal must be grown and then exposed to X-rays while the crystal is
rotated through many different orientations. The X-rays used in these experiments are
simply light (i.e. photons) with a wavelength of near 1 Å (10-10 m). During X-ray
diffraction, the electrons in the protein interact with the X-rays to scatter them and create
a diffraction pattern, which is collected by an X-ray sensitive detector (for instance, a
CCD detector). When the sample is a crystal, the scattered X-rays typically create a
pattern of lunes (see Figure 3 step 3). The data needed to solve the structure are derived
from this pattern by assigning a unique identification index (in the form of the integers h,
k, and l) and measuring the intensities to each of the diffraction spots (also known as
‘reflections’).
The assignments and intensity calculations are done using computer programs.
The CCP4 (Collaborative Computational Project Number 4; 31) package is a collection of
computer programs that we used for many of the X-ray crystallographic calculations,
from analyzing the data to applying final refinements to a completed protein model. The
program we used to process the diffraction data is called iMosflm.32,33 It takes a subset of
the reflections and determines the space group, an inherent characteristic of the crystal
that describes the symmetry of protein molecules in the crystal. This space group
19
Fig. 3: Overview of X-ray Crystallography. The process of determining the 3dimensional structure of a protein involves obtaining a purified protein solution. Due to
the FAD cofactor, the solution of MpGlpO was yellow. This protein is mixed with
chemical solutions and placed in a sitting-drop well plate. If crystals form, these can be
exposed to X-rays where a detector collects the diffraction pattern. The information from
the diffraction pattern is combined with phases from a structurally similar model to solve
the phase problem. Many rounds of refinement are then performed until the structure very
closely matches the density.
20
identification is used to predict the location of the reflections that make up the diffraction
pattern. For each reflection, the program integrates the spot to determine the intensity.
Because the complete sets of diffraction data that are collected contain many independent
measurements of each unique reflection, once the spots are all integrated, a program
called Scala within the CCP4 suite of programs can be used to scale the reflections to one
another and average together the multiple observations of each kind of reflection. In my
crystal’s space group, P23, there was a high level of symmetry. This meant that, as the
crystal rotates, many symmetry-related reflections are duplicated and must be merged.
The merging function of Scala creates both average reflection intensities and variance
statistics. It is with the statistics generated from this program that it is possible to decide
which data are accurate enough to use and which should be discarded. The major
decision in this regard is choosing a resolution cut-off, which defines the highest
resolution (e.g. 2 or 2.5 or 3 Å) at which the data are still of acceptable quality.
After getting the intensities, the CCP4 Truncate program converts them into
structure factors, also called Fobs (for ‘F-observed’), by, in most cases, simply taking the
square root of the intensities. The Fobs is the amplitude of the wave form of the light ray
that generated the given reflection. Light acts as a wave and from the experimental
intensities, as noted, we can obtain the height (or amplitude) of the wave. We cannot,
however, obtain the phase of the wave. Because this phase information is lost, it leads to
the so-called ‘phase problem.’ Solving this problem, that is figuring out the phases, is
important because both phase and amplitude information are necessary to apply a Fourier
transform that converts the data from the diffraction pattern into a form that recreates the
21
real space electron density distribution. This is the density in which we model a protein
structure.
In my case, to solve the phase problem and be able to piece the puzzle together, I
used molecular replacement. In general, a protein structural homolog that has already had
its structure determined (that is, a known protein structure that looks like the protein
structure to be solved) is repositioned at multiple possible places in the unit cell until it
can roughly account for the observed diffraction data. Using this best position of the
search model, the placed protein model can be used to calculate initial phases.34 In
determining a protein structure via molecular replacement, there is the inherent issue of
model bias. The goal is to get a structural representation of the desired protein (MpGlpO)
based on the data, and to not have that model be skewed based on the structure of the
search model. To be able to detect bias that may occur, 5% of the reflections are set aside
prior to molecular replacement and the model building and refinement steps, and these set
aside reflections are used for cross-validation – the process of validating a model based
on data that were not used to determine it. Refinement is the iterative process of
improving the phases and model by making slight adjustments that improve the model’s
fit to the observed data. The statistic Rfree is the measure of agreement of the model with
the 5% of the reflections that are not utilized in the refinement. Thus, this statistic is
independent of manual remodeling and is referenced to ascertain if refinement has
brought the model closer to the real solution. Rfree is reported along with the R-factor in
refinement statistics. Lower values are better and R-values typically range from 0.6
(indicating total disagreement of the model and data) to as low as 0.10 to 0.20. These
22
lower values indicate that the model is sufficiently close to the observed data to be
acceptable as a final model.
Typically, the real differences between the unknown structure and the search
model can be modeled into the electron density computed from molecular replacement by
running an ‘AutoBuild’ program and refinement through which the computer is able to
optimize the changes made to the model. However, if the protein search model is too
dissimilar, manual model building is required in place of AutoBuild. This is a process in
which each individual position in the protein is looked at by the researcher to determine if
the electron density supports a change in the structure. This can involve a change in the
amino acid or side chain itself or in the pathway of the main chain. This manual
rebuilding process is continued until no additional changes supported by the density can
be made.
Cloning and Protein Purification
Concentrated and purified His6-tagged MpGlpO was acquired from collaborators
in the laboratory of Dr. Al Claiborne at Wake Forest University. The protein was sent at a
concentration of 10 mg/mL in a buffer of 50 mM potassium phosphate, pH 7.0, and 0.1
mM EDTA. Having the purified and concentrated protein available allowed me to
proceed with the next step of growing crystals.
Crystallization, Data Collection, and Data Processing
Crystallization was carried out using the sitting drop and hanging drop methods,
which allow the buffer conditions in which a protein is dissolved to slowly change as a
23
drop of solution containing the protein slowly evaporates to achieve equilibrium with a
reservoir solution. Upon being trained in how to use the Phoenix crystallization robot
developed by Art Robbins 35, I set up initial crystallization trials using 96 different buffer
conditions that are commercially available in the ‘Hampton Index’ crystallization
screen.36 These experiments are set up in special 96-well plates, which for each buffer
condition, allows for three different mixing ratios of protein stock and the reservoir. This
first screen used protein reservoir mixtures as follows drop 1 - 0.25 0.50 L drop 2 0.25 0.25 L drop 3 - 0.50 0.25 L. Within a week at 4 C, yellow crystals formed in
some conditions, but since a large portion of the drops had precipitated protein, we
concluded the protein concentration was higher than would be ideal. We thus diluted the
protein by 50% and prepared further 96-well plates using the above ratios of drops, again
using the Hampton Index for a control, as well as using other commercial reservoir
varieties (Hampton Crystal Screen I and II, Precipitant Synergy, Wizard I and II, and
Wizard III and IV). A representative crystal (originally designated cpaj), grown in a
reservoir of 2.68 M NaCl, 3.35% v/v isopropanol, and 0.1
HEPES pH 7.5 (from
Precipitant Synergy Screen 7) at 4 C, was harvested.
The crystal was then exposed to X-rays at a laboratory X-ray source at Oregon
State University using Cu-Kα X-rays from a Rigaku rotating anode generator set at 50 kV
and 100 mA and a Raxis IV X-ray detector. The initial diffraction pattern indicated that
the crystal was in fact protein (as inferred from the spots being relatively close together)
but that it was useable only out to ~6 Å resolution, insufficient to determine a protein
structure. An assessment of crystal formation gave us leads to pursue. There was a
distinction made in the shape of the crystals because this affected diffraction quality.
24
Those that were sharp and pyramidal gave better resolutions and statistics than those that
were rounded.
Lead reservoir conditions were optimized by using 24-well hanging drop plates
with a 400 L reservoir volume. Optimization consisted of making new reservoir
solutions that were altered slightly from the original conditions and comparing the quality
of the new crystals formed versus the initial lead from the 96-well plate. This produced
numerous useable crystals. This process was repeated for the top 3 leads. Yellow,
tetrahedral-pyramidal crystals (like that shown in Fig. 3) were grown from one lead
optimization tray within a week. The largest and sharpest crystals (what we hoped would
be of the best diffraction quality) were grown in 2.5 M NaCl, 0.1 M Imidazole (pH 7.15)
at 4 C with a ratio of 1 L of protein 2 L of reservoir.
One crystal (named cpaz in files, here called GlpOox1), of size 0.35 mm x 0.35
mm x 0.35 mm, was retrieved out of this solution. Collected in a small loop, the crystal
was cryo-protected with oil, submerged in liquid nitrogen, packaged, and sent to a
synchrotron source: the Advanced Light Source at Lawrence Berkley National
Laboratory in Berkley, CA. There, a complete dataset was collected (360 images, 300
mm detector distance, λ = 1.0 Å). This was a case where I was fortunate to have wellbehaved crystals. They grew quickly (within a week) and were not destroyed upon my
manipulation to retrieve them with the loop or with exposure to X-rays. No observable
flavin reduction was observed as crystals remained yellow after data collection. A
reduced crystal would appear pale yellow or even opaque. Further, they lasted long in
solution if kept at 4 C, allowing us to extend our resources and keep the crystals over
many months for use in later experiments.
25
The initial diffraction patterns had diffraction spots that were observed to be out
to ~2.7 Å resolution. The images were input into iMosflm and processed. The crystal
GlpOox1 was indexed as space group P23 and integrated with a unit cell of a=b=c =
112.18 Å and α, ,
90. The resulting integrated data were then input into the CCP4
program Scala to scale and merge the symmetry related reflections to give the final
intensities. Based on data statistics given by the program and general recommendations
for resolution cut-offs, we determined that GlpOox1 data set to have a resolution limit of
2.5 Å. For data collection statistics, see Table 1.
An initial structure was determined using data from GlpOox1. However, because
of the limit of data quality, we tried collecting data from additional native crystals in
order to get higher resolution data. This would allow us to get a more accurate
representation of the structure of MpGlpO. Yellow, trigonal-pyramidal crystals of ca. 0.3
mm x 0.3 mm x 0.3 mm were obtained within a week in reservoir conditions of 2.68 M
NaCl, 0.1 M HEPES (pH 7.5), and 2% v/v isopropanol (optimized from Precipitant
Synergy Screen 7) at 4 C. Ice content can also affect data quality so we optimized the
cryo-protectant which serves to protect the crystal while in liquid nitrogen. Solutions of
AML mixed with varying concentrations of glycerol were prepared and similar crystals
were allowed to soak in each condition for three minutes. Results of this experiment are
shown in Figure 4. 15% glycerol in an AML solution was chosen as the optimal cryoprotectant because it was the smallest concentration in which few ice rings were detected.
A representative crystal (called cpck in files, here called GlpOox2) was scooped out of
the drop and placed in artificial mother liquor (AML) of 3.0 M NaCl, 0.1 M BisTris (pH
7.0) for one hour. The crystal was then stored for 3 minutes in the AML plus 15%
26
Table 1: Data Collection and Refinement Statistics for GlpOox and GlpOred
GlpOox
GlpOred
X-ray wavelength (Å)
0.9765
0.9765
Space group
P23
P23
Unit cell axis length (Å)
111.59
111.61
Resolution range(Å)
49.90-2.40
55.80-2.50
High resolution bin range (Å)
2.53-2.40
2.64-2.50
No. of reflections
430576
836296
No. of unique reflections
18401
16368
Completeness (%)
99.79
100
Multiplicity
23.4 (23.6)
51.1 (28.6)
Rpim
0.022 (0.185)
0.072 (0.281)
Rmeas
0.109 (0.900)
0.533 (1.514)
I/σ
5.7 (0.90)
0.7 (0.50)
Rfactor (%)
15.8
16.4
Rfree (%)
21.4
22.6
Number amino acid residues
384
384
Number solvent atoms
201
202
Total number atoms
3305
3285
Average B (Å2) protein atoms
30.8
46.1
Data quality statistics
Refinement statistics
* Values in parentheses are for the high resolution bin
27
Figure 4: MpGlpO Cryo-protectant Optimization Images. Crystals were soaked in a
variety of glycerol concentrations to determine the optimum conditions for data
collection. A) Initial harvesting was performed with oil and no glycerol. Ice rings circle
the data and can reduce data quality. B) Ice rings still persist when the crystal was soaked
in 10% glycerol. C) No large ice rings are visible when soaked in 15% glycerol. D) The
diffraction pattern when the crystal was soaked in 20% glycerol looks similar to that with
15% glycerol. We chose to use 15% glycerol for harvesting because it produced the
cleanest diffraction pattern with the least amount of glycerol.
A: Crystal cpaz dragged through oil- 0%
glycerol
B: Crystal cpbk soaked in 10% glycerol
for 3 min
C: Crystal cpbm soaked in 15% glycerol
for 3 min
D: Crystal cpbq soaked in 20% glycerol
for 3 min.
28
glycerol. This crystal diffracted to 2.4 Å resolution, meaning it gave us more useful
information. The unit cell dimensions and data quality statistics for GlpOox2 can also be
found in Table 1. Reduced crystals and substrate soak crystals were grown in similar
conditions. This experiment was performed in order to elucidate how the structure may
change with FAD reduction or ligand binding. Ligand binding in particular is important
for structure-based drug design because it may give insight into how a potential drug
binds to inactivate the enzyme. We soaked crystals in 10 mM dithionite in a degassed
AML or in 10 mM of the respective substrate in a 15% glycerol AML for one hour. A
total of 24 crystals were either soaked in one of four substrates or reduced via two
different methods. We collected 18 datasets. For substrate soak conditions and their
resolution limits see Table 2. Two data sets of dithionite-reduced crystals (designated
cpcy and cpcz in files, here called GlpOred1 and GlpOred2 respectively) were collected
out to 2.5 Å resolution (240 images, 360 mm detector distance, λ = 1.0 Å) and the
reduced data from the two crystals were merged together to produce a single data set with
better data quality. The substrate-soaked crystals were similarly exposed to X-rays at the
synchrotron source and data were collected, although none of them showed any ligand
binding.
Structure Determination and Refinement of Oxidized MpGlpO
Molecular replacement
The intensities of the diffraction data were converted into structure factor
amplitudes (Fobs) using Truncate from CCP4. However, subsequent attempts using these
structure factors to run molecular replacement using the GlpO model from Steptococcus
29
Table 2: Overview of MpGlpO with Soaked Substrates and Results. The results of
numerous ligand soaks are presented. Closely identical native crystals were grown and
soaked in a variety of conditions in an attempt to get ligands to bind. All harvesting
conditions mirrored that of the native crystal, having both AML and 15% glycerol with
no ligands present. Box shading indicates the color of the crystals after soaking. No
bound substrates were seen in the protein structure.
Soak
Crystal ID
Harvesting
Conditions
Substrate Bound?
Resolution
Native
cpck
1 hour in AML with
15% glycerol
No
2.4
2phophoglycerate
cpdg
cpdh
cpdj
G3P
cpcl
cpcn
PEP
cpch
cpcj
Dithionite+mv
cpde
No
2.6
2.6
2.7
No
3.0
2.7
No
2.9
3.2
10 mM dithionite,
0.5 mM methyl
viologen for 1 hour
in degassed AML,
15% glycerol
2.7
No
2.9
cpdf
2.7
cpcy
2.7
cpcz
cpda
Tartaric Acid
10 mM G3P for 1
hour, 3 minutes in
AML + 15%
glycerol
10 mM PEP for 1
hour, 3 minutes in
AML + 15%
glycerol
2.5
cpdc
cpdd
Dithionite
10 mM 2phosphoglycerate
for 1 hour, 3 minutes
in AML + 15%
glycerol
10 mM dithionite for
1 hour in degassed
AML, 15% glycerol
2.5
N/A
2.6
cpdb
2.6
cpcr
2.8
cpct
cpcv
10 mM tartaric acid
for 1 hour, 15%
glycerol
No
2.8
2.7
30
sp. (PDB entry 2RGH) were unsuccessful. We initially thought that the problem was the
parameters set in the program, so we adjusted those to no avail. We then tried modifying
our search model of SsGlpO. Sometimes, replacing each amino acid residue with an
alanine can provide enough structural similarity to allow the program to find an
acceptable initial model. However, this was a very inefficient procedure and we decided
to tackle it if other strategies did not help. Another idea was to increase the number of
molecules to search for in the asymmetric unit (ASU). The ASU is the basic unit of the
crystal that, when translational and some rotational operations are applied, forms the
complete crystal. To solve a protein structure one must only model the unique
information that is contained in the ASU. The programs that initially failed were directed
to try and find one MpGlpO molecule in the ASU based on a prediction made by the
Matthew's Probability Calculator.37,38 Since this was just a prediction, we tested whether
increasing the number of search models would be sufficient to calculate the phases.
Again, this was of no success. A final issue was addressed when we explored the output
log files of the two failed programs. As it turned out, the molecular replacement program
was not detecting the presence of reflections. This means that the program was working
without any diffraction data. Working backwards, we determined that the error was in the
Truncate program from CCP4, which due to a bug was outputting an empty file. We thus
used an older version of the program and then were successful in getting the molecular
replacement programs to recognize the reflections. This really taught me to look at the
log output files in troubleshooting.
One important lesson from this detour was the importance of having a structurally
similar search model. We were skeptical that the level of sequence similarity with
31
SsGlpO was enough, so I ran a database BLAST39 search to look for any more similar
protein homologs. The solved structure that was the most similar to MpGlpO in sequence
was one from Bordetella pertussis (PDB entry 3DME) with a similarity score of 89.4 bits
and a level of sequence identity of 27%. Throughout this thesis, this structure is referred
to as Bp3DME. SsGlpO wasn't even on the top 100 list and was thus presumed to not be
the best choice to use as a search model for molecular replacement. An alignment of the
sequences of SsGlpO and MpGlpO reveal a similarity score of only 21.2 bits. A high
sequence identity was given but is only valid for 34% of the protein. However, we did
suspect that SsGlpO could be a useful search model if we could extract just the core GlpO
protein. We compared SsGlpO and 3DME using Pymol40 and were able to create a
modified SsGlpO model that was missing the C-terminal domain (amino acids 453-606),
the first α-helix (amino acids 1-17), an extraneous loop (amino acids 241-251) and waters
to create ‘SsGlpO-truncated.’ Sequence alignment of SsGlpO-truncated to MpGlpO still,
however, did not produce a reasonable alignment without numerous gaps in the
sequences.
Molecular replacement was tried using either SsGlpO-truncated or Bp3DME with
one, two, or three molecules in the ASU as parameters. However, it was not until we
switched to the Auto-MR program of Phenix (another computer suite of programs in Xray crystallography; 41), that we were successful in getting an initial model that was a true
solution. Chain A of Bp3DME was used as a search model in which to calculate initial
phases based off the structure factors of the MpGlpO data. The log-likelihood gain (LLG)
was 57.7. This is a measure of how much better the model is in comparison to a random
distribution of the same atoms.34
32
It was essential to verify our solution and we did this by having an internal
control. The GlpO activity of SsGlpO is due to a FAD molecule.30 Bp3DME also contains
an FAD molecule, even though the function of the protein is currently unknown. By
deleting the FAD from the model in refinement, we could be sure that any density that
was around the FAD region was not due to model bias. Further, we identified a key
location where a larger amino acid (such as a glutamate at residue 21 in MpGlpO) was
supposed to be in the location of a smaller amino acid (residue alanine 20 in Bp3DME),
based on the sequence alignment (see Figure 13 in results/discussion). Rather than just
accepting the model from the first refinement, we ran the same refinement program
multiple times but with slightly different parameters and were able to pick the model with
the best density to build into and refine. This process continued until specific parameters
were narrowed down as most beneficial for refinement. Pictures of both the FAD and
glutamate 21 in their electron density from the initial refinements and the final structure
can be seen in Figure 5. The large peaks of electron density in the location where the
MpGlpO should have had more atoms gave us confidence that the molecular replacement
solution was real. We could then proceed to refinement of the model to complete it and
improve its fit to the density.
Manual modeling and refinement – stage 1: refinement of GlpOox1 model
Once a molecular replacement solution has been verified, in many cases, the
model refinement can be quickly completed if there is high similarity between the search
model and the structure being solved. 3DME, however, had only a 27% sequence identity
with MpGlpO. As such, attempts to automatically convert the amino acids of 3DME into
the amino acids of MpGlpO were unsuccessful. Manual modeling of MpGlpO was
33
Fig. 5: Ala 22 Comparison and FAD Internal Control
a) Initial model of MpGlpO for comparison of Glu 21 (MpGlpO numbering) before
manual remodeling. Side chain of Glu 21 was modeled into the large empty peak of the
2Fo-Fc.electron density map. b) Same region of the completed MpGlpO model. c) A
region of electron density when FAD was not in the input model. d) A flavin was placed
over this electron density to show that this piece of the FAD molecule roughly fit within
the shape of electron density. e) Region of strong electron density. When FAD was
placed in the model to align with the electron density in the first panel, the
pyrophosphates of the FAD aligned exactly within this strong electron density. This
indicated that FAD belonged in that place in the model and that our molecular
replacement solution was valid. f) Poor electron density for the FAD in an initial
structure is contrasted with the strong electron density for the FAD in the final structure.
b)
a)
c)
f)
d)
e)
34
therefore the only viable option to pursue. This involved investigating each individual
amino acid position, assessing which amino acid should be substituted in, and then doing
so only when the electron density map gave evidence for this change. Both visualizing
and modifying of the model were done in a program called COOT (Crystallographic
Object-Oriented Toolkit; 42). Because the experimental electron density map was the best
indicator of what model features would satisfy the observed data, it was essential to
follow where the density guided. While this was more laborious and time-consuming,
walking through the protein allowed me to appreciate the intricacies of the structure and
understand how each individual amino acid residue plays a role in determining the threedimensional structure. This process also taught me about protein chemistry and geometry.
One important lesson was not to put full trust in a sequence alignment. There were many
instances where a gap or insertion in the alignment I had originally created was not
verified by the density of the structure. A table summarizing the many rounds of model
building and subsequent computational refinement statistics is given in Table 3. After
determining that Bp3DME was a suitable search model, six more rounds of simulated
annealing and minimization refinements were performed with GlpOox1 (with both
Cartesian and Torsion Simulated Annealing 41) coupled with manual model building
using COOT, and leading to an improved model with R and Rfree values of 0.32 and 0.45
respectively.
Manual modeling and refinement – stage 2: addition of GlpOox2 data
As mentioned previously, we continued to collect data on additional crystals in
order to get better diffracting crystals. One such crystal, GlpOox2, yielded data useful to
a resolution of 2.4 Å. The intensities were converted to structure factors, and Rfree flags
35
Table 3: Rounds of Refinement. A table of the pathway of model building and
subsequent computational refinement statistics is presented. Starting with round 9, the
GlpOox2 data were used. As refinement progressed, the number of atoms placed
increased while R and Rfree decreased. Starting at round 13, a variety of interpretations
were tried for the strong electron density at the crystallographic 3-fold (listed in the last
column labeled “ etal”).
Refinement
Round
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Description
AutoMolecular
Replacement
Cartesian
Simulated
Annealing
Cartesian
Simulated
Annealing
Cartesian
Simulated
Annealing
Torsion
Simulated
Annealing
Minimization
Torsion
Simulated
Annealing
Buster
refinement
with new 2.4Å
data
Buster
refinement no manual
rebuild in
Buster-10
Cartesian
Simulated
Annealing
Buster
refinement
Buster
refinement
Buster
refinement
Buster
refinement
Buster
refinement
Buster
refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
TLS and
Restrained
Refinement
Rounds of Refinement Table
Number
Number
of
of
residues
atoms
File Name
Program
Resolution
limit (Å)
Solvent
Molecules
R
initial
AutoMR_run_2
CCP4
N/A
363
2688
0
refine_10
Phenix
3.0
366
2688
0
0.5574
0.3897
0.5538
0.5215
-
refine_21
Phenix
3.0
366
2688
0
0.4291
0.3370
0.5356
0.5191
-
refine_37
Phenix
3.0
366
2741
0
0.6869
0.3498
0.5235
0.4877
-
refine_45
Phenix
3.0
366
2693
0
0.3998
0.3172
0.4800
0.4583
-
refine_63
Phenix
2.8*
resolution
cut off
within
program
347
2570
0
0.3925
0.3359
0.4731
0.4758
-
refine_72
Phenix
3.0
347
2570
0
0.3754
0.3153
0.4442
0.4494
-
Buster-10
Buster
3.0
326
2294
0
0.3928
0.3835
0.4364
0.4198
-
Buster-11
Buster
2.4
326
2294
0
0.4116
0.4008
0.4370
0.4328
-
refine_92
Phenix
2.4
363
2702
0
0.3947
0.3131
0.3830
0.3786
-
Buster-15
Buster
2.4
363
2702
0
0.3092
0.3333
0.3801
0.3751
-
Buster-16
Buster
2.4
372
2899
0
0.3157
0.2790
0.3299
0.3091
-
Buster-18
Buster
2.4
384
3045
0
0.2685
0.2324
0.2817
0.2763
Na
Buster-19-3
Buster
2.4
384
3028
65
0.2271
0.2010
0.2421
0.2456
Na
Buster-20
Buster
2.4
384
3068
150
0.2138
0.1840
0.2192
0.2238
Na
Buster-21-1
Buster
2.4
384
3059
185
0.2050
0.1752
0.2133
0.2222
Na
Refmac_83
Refmac
2.4
384
3104
191
0.2225
0.1603
0.2411
0.2057
K
Refmac_87
Refmac
2.4
384
3112
195
0.2165
0.1605
0.2536
0.2113
SO4-
Refmac_89
Refmac
2.4
384
3107
198
0.2145
0.1595
0.2567
0.2103
H2O
Refmac_91
Refmac
2.4
384
3108
198
0.2152
0.1588
0.2510
0.2130
Zn-S
Refmac_104
Refmac
2.4
384
3109
200
0.2169
0.1590
0.2554
0.2128
ZnH2O
Refmac_105
Refmac
2.4
384
3109
203
0.2110
0.1581
0.2559
0.2141
Ni-Cl
Refmac_110
Refmac
2.4
384
3109
199
0.2128
0.1581
0.2595
0.2150
Ni-Cl
Refmac_122
Refmac
2.4
384
3109
200
0.2097
0.1578
0.2589
0.2139
NiH2O
R final
Rfree
initial
Rfree
final
Metal
-
36
were imported from the previous work done using GlpOox1. We did not want to start
back at the beginning of molecular replacement since we had already made real changes
in the search model structure that were validated by the density of MpGlpO. We were
able to extend the work we had already accomplished by merging the data and importing
the Rfree flags. Refinement using these new data, however, continued using the same
strategy involving rounds ofmanual model rebuilds. With the improved data quality, the
model quality indicators R and Rfree decreased quickly, as shown in Figure 6. Sometimes
in manual refinement, large changes to the protein structure do not change the refinement
statistics substantially, leaving values around the 0.4 to 0.5 region. Incorporation of this
improved data from GlpOox2 helped me to get over the hump of manual refinement and
see drastic improvements in the phases and electron density that enabled me to model
MpGlpO more effectively.
Manual modeling and refinement – stage 3: final adjustments and metal chemistry
Using the techniques described above, the main chain and most side chains of
MpGlpO were modeled. Water molecules were added after round 13 with the following
criteria: (1) a peak of ≥ 1 ρrms in the electron density map, and (2) a distance of ≥ 2.2 Å
and ≤ 3.6 Å between the water and nearby hydrogen-bond donor or acceptor. On the last
round of refinement, water molecules were renumbered in accordance with their density
with water-1 having the highest electron density. Residue Lys79 was left in a stubbed
state (i.e. modeled as an Ala). This is a specific example of where we didn’t have
evidence for where to place the side chain because little to no density was present for
atoms beyond the C -atom. MolProbity 43 and other verification tools in COOT were
utilized in later stage refinements. One large puzzle was figuring out the meaning of an
37
Figure 6: Progress of Model Building and Refinement as Monitored by R and Rfree.
The R and Rfree values are plotted against the round of refinement. With the iterations of
manual model building, both R values decreased until leveling out around 0.15 and 0.21,
respectively.
0.6000
0.5000
R-value
0.4000
0.3000
Rfree
R
0.2000
0.1000
0.0000
0
5
10
15
Refinement Round
20
25
30
38
unexpected electron density peak at a three-fold symmetry axis, near the side chain of
His59. An anomalous difference map calculated using the CCP4 suite of programs gave a
peak suggesting it was a metal. Then, remotely we conducted a set of fluorescence scans
using the synchrotron at Lawrence Berkeley National Laboratory that were near
wavelengths appropriate for various metals, helping us identify the metal as a nickel ion
(see Figure 10). Manual modeling and Refmac refinements were continued to give a final
R and Rfree values of 0.158 and 0.214, respectively, for the oxidized, native MpGlpO after
24 rounds of refinement. Alternate side-chain or Cα conformations were modeled for
residues Gln40, His244, Trp375, Asn376, and Gly377.
Diffraction data from a crystal chemically reduced by 10 mM sodium dithionite
was combined and processed, with the Rfree test set imported from the oxidized GlpO data
set. Rigid body refinement was performed to give R and Rfree of 0.2059 and 0.2426,
respectively. A similar method was performed with dithionite reduced data with added
methyl-viologen. An Fo-Fo difference map of the dithionite reduced data minus the
oxidized GlpO data was created and analyzed to find structural changes caused by the
reduced state. Minor changes were made to the model. Manual modeling and Refmac
refinements were carried out to give a final R and Rfree values of 0.164 and 0.226,
respectively.
For each substrate soak data set collected, an Fo-Fo difference map was created to
visualize the density differences between GlpOox2 and the soaked crystal. A search for
bound ligands was primarily centered on the active site, above the flavin.
39
Structural Comparisons
Once the completed oxidized model was built, we compared MpGlpO to other
proteins with similar structures. Using the Dali server44 we identified four structures of
interest. We compared Bp3DME, SsGlpO, EcGlpD (an aerobic glycerol-3-phosphate
dehydrogenase with PDB code of 2QCU), and BsGlyOx (a representative glycine oxidase
with PDB code of 1RYI). The Dali results were also used in creating a structure-based
sequence alignment. I tried multiple programs, but with Dali, only some modification
was needed to create an alignment that worked well for all five structures. DSSP45 and
Pymol40 were used for defining the secondary structure assignments that were used to
make a topology diagram.
40
CHAPTER 3
RESULTS AND DISCUSSION
In this section, I describe the overall structure and make comparisons between
MpGlpO and proteins with similar structures, aiding us in understanding how this
enzyme works and leading to evolutionary and drug design implications.
Enzymatic Activity and Oligomeric Structure in Solution
Recombinant MpGlpO was successfully expressed and purified as an Nterminally His-tagged protein, and certain solution properties of recombinant MpGlpO
have be assessed by collaborators in the laboratories of Dr. Al Claiborne (Wake Forest
University) and Dr. Pimchai Chaiyen (Mahidol University, Thailand). Although these
experiments are not documented here, I summarize them briefly as they provide an
important part of the context of the structural studies. One FAD resides in each enzymatic
chain. The native molecular mass of MpGlpO is 42 kD based on gel filtration analysis,
and compared with the calculated subunit molecular mass of 45 kD, this indicates that
MpGlpO is a monomeric enzyme. This is intriguing because members of the structurally
and sequentially similar DAAO family are dimeric enzymes.
The first published data about the activity level of MpGlpO indicated that it is in
fact an oxidase instead of a dehydrogenase.3 Unpublished, preliminary data from the
laboratory of Dr. Al Claiborne indicates that MpGlpO has an activity level of 12.8
units/mg of protein. This is approximately 25% of the activity of SsGlpO, for which the
specific activity is 60.3 units/mg of protein. Since the structure of Bp3DME was solved
by the NorthEast Structural Genomics group, there was no publication reporting on the
41
enzymatic activity in this enzyme. The Claiborne group has shown it has no GlpO
activity (also unpublished data).
Solution of the MpGlpO Structure
The structures of native MpGlpO in both the oxidized and the reduced flavin
states have been determined at 2.4 Å and 2.5 Å resolution, respectively. The oxidized
structure was determined by molecular replacement (see methods) and refined to a final
R of 0.158 and Rfree of 0.214, and the reduced structure was solved by a simple difference
Fourier analysis and refined to a final R of 0.164 and Rfree of 0.226 (see Table 1).
The modeled structure of oxidized MpGlpO consists of 384 amino acids,
including residues 1-384 modeled for the single chain in the asymmetric unit, one FAD,
201 water molecules, and one nickel ion. The His-tag was not visible in the structure.
There are a few places in the structure in which an alternate backbone pathway or side
chain conformation was supported by the electron density. One such alternate pathway
was modeled from Trp-375 to Gly-377 (Fig. 7). As model building progressed, a strong
Fo-Fc density peak near Asn-376 matched with a shifted position of Asn-376. In order to
properly fit this density, Trp-375 and Gly-377 were also duplicated and allowed to adjust.
This alternate pathway was important to model because the protein, as it moves and
functions, has an equal chance of being in either of these paths.
In a protein structure, it is also common to see alternate conformations for some
amino acid side chains. This can best be seen in MpGlpO with His-244. Due to bond
geometry and atomic interactions, there are only certain local side chain conformations
that are favorable. The electron density gave evidence that the imidazole ring of the
42
Fig. 7: An Alternate Backbone Conformation Example. Because proteins are mobile,
they can take a variety of equally likely paths. In MpGlpO, there was electron density to
support 376-Asn in both the green and cyan positions. In order to place the cyan amino
acid, 375-Trp and 377-Gly were both slightly adjusted. As a result the protein path
appears to diverge and then converge.
43
histidine residue, the bulk of this amino acid, could take on either of two conformations
(Fig. 8). Since the 2Fo-Fc density for each of the two positions had roughly the same peak
height, we concluded that each of the alternate side chains occur 50% of the time in this
crystal form of the protein. In addition to the alternate conformations, the Lys-79 side
chain was truncated to an alanine because, while the main chain could confidently be
modeled, the side chain had very weak electron density and couldn’t be modeled. Since
Lys-79 is a surface residue, we presume that it is too mobile to model.
Interestingly, at the crystallographic 3-fold axis, three symmetry-related imidazole
side chains of His-59 coordinate a peak of strong density (Fig. 9a and 9b). Analysis of an
anomalous difference map provided significant evidence that this is a metal ion (Fig. 9b).
In order to identify this metal, we performed a fluorescence scan on the crystals and saw
a signal at the K edge for nickel (Fig. 10). As shown in Figure 9a, the atomic distances of
Ni at this site and with a water molecule as a fourth ligand fit within typical Nicoordination guidelines. As this was a His-tagged protein purified over a Ni-affinity
column, we suspect some Ni could have come from the column and remained in the
protein solution after purification. This contamination was fortunate because, based on
the location of the metal at the crystal contact, crystal formation may have only happened
because of its presence.
Overall Structure
In terms of the tertiary structure, MpGlpO is, as expected, very similar to the
Bp3DME protein used as a search model and to the Streptococcal GlpO (Figure 11).
44
Fig. 8: An Alternate Side Chain Conformation Example. Proteins can exhibit alternate
side chains where pieces of the side chain reside in equally likely rotamer positions. 244His of MpGlpO has one conformation of the imidazole head in green and another one in
cyan.
45
Fig. 9: Nickel Ion Coordination and Potential Dimer Interface. A) Ni is bound
between three imidazole rings of 59-His in three different molecules. The distance
between the Ni and all three imidazole rings is 1.98 Å. A water ion also coordinates to
the Ni. This distance is 2.17 Å. B) Packing of the three molecules of MpGlpO around the
Ni ion at a three-fold axis in the crystal is shown along with the anomalous difference
map peak for the metal (contoured at 14.50 ρrms). Helix α3 is near the interface. C) A
stereo image of the most extensive crystal packing interface that represents a potential
dimer interaction. The interface combines two beta sheets to make a new, larger beta
sheet interface. The Ni ion (shown with His side chain) is on the opposite side of the
potential dimer interface.
A
B
H2O
2.17 Å
Ni
1.98 Å
59-His
C
46
Fig. 10: Synchrotron Source Scan of MpGlpO aids Identification of the Metal Ion.
Shown is the fluorescence scan with the X-ray wavelengths tuned to the absorption edge
of nickel. After trying scans at the absorption edges of various metals, Ni was the only
metal which had a clear signal. The known value for Ni is 8339 eV (indicated by the
arrow).
88000
86000
84000
Intensity
82000
80000
78000
76000
74000
72000
8280
8290
8300
8310
8320
8330
8340
8350
X-ray Energy (eV)
Ni Trial 1
Ni Trial 2
8360
8370
8380
8390
47
Fig. 11: The Topology of MpGlpO, SsGlpO, and Bp3DME. A) MpGlpO folds into two
discontinuous domains, with a 9-stranded -sheet in one domain (blue in the topology)
and a 6-stranded -sheet in the other (orange). Both of these -sheets are mostly antiparallel. The latter domain also has an additional 3-stranded -meander. B) SsGlpO has a
similar topology to MpGlpO but it has an additional large C-terminal domain also found
in similar glycerol 3-phosphate dehydrogenase enzymes. SsGlpO lacks two beta strands
that bridge the blue and orange domains. C) Bp3DME also lacks the large C-terminal
domain and closely resembles the structure of MpGlpO.
A
48
B
SsGlpO
C
Bp3DME
49
MpGlpO folds into two discontinuous domains, with a 9-stranded -sheet in one domain
(blue in Fig. 11A) and a 6-stranded -sheet in the other (orange in Fig. 11A). Both of
these -sheets are mostly anti-parallel. The latter domain also has an additional 3stranded -meander. The domains are connected in two main regions, one more ordered
and one more mobile. The more ordered region consists of helix α2 and surrounding
secondary structures. Both MpGlpO and Bp3D E have two beta strands around the α2
that are not present in SsGlpO. SsGlpO has an overall similar topology but has an
additional helical domain at its C-terminal end (red in Fig. 11C). As observed by the
three topology diagrams, most of the 2-domain structure of MpGlpO is conserved in
Bp3DME and SsGlpO. In the protein structure, the domains are wrapped around each
other in a way that leaves a big pocket for binding of FAD (Fig. 12).
Using the PISA server46, we analyzed the packing interactions between MpGlpO
molecules in the crystal to see if any of the interfaces could be physiologically relevant.
The expected FAD: MpGlpO interface was observed. If MpGlpO were a monomer, this
would be expected to be the only significant interaction. However, one very large
protein:protein interaction in the crystal represents a potential MpGlpO:MpGlpO dimer
interface with approximately 1600 Å2 interaction surface (Figure 9c). While inconsistent
with the gel filtration analyses described above, it is consistent with both Bp3DME and
SsGlpO, both of which are dimers. Members of the DAAO family are also known to be
dimers. Comparing which secondary structures interacted to make up the dimer gave
intriguing results. Strand 15, part of the 8 stranded
sheet on one MpGlpO molecule,
interacts with the same 15 strand on a rotated molecule of MpGlpO (the -x, y, -z+1
symmetry mate) to make a 16 stranded
sheet across the dimer (Fig. 9c). In contrast,
50
Fig. 12: Stereo Ribbon Diagram of MpGlpO. Shown is a ribbon diagram of MpGlpO
where the α-helices are represented by spirals and the -sheets are represented by arrows
pointing in the C-terminal direction. The FAD cofactor is shown in yellow. In this view,
an open pocket for flavin interaction is easily seen.
51
Bp3DME forms a dimer (with a second molecule in the asymmetric unit of the crystal)
via interactions involving helix α6, and SsGlpO forms a dimer via interactions involving
helices α2 and α3 (with the -x+1, -y, z symmetry mate). Since the potential dimer
interface of MpGlpO is not conserved in either of these most closely related proteins, and
since solution studies indicate it is a monomer, we conclude that the dimer packing
interface seen in the MpGlpO crystals is not physiologically relevant. However, if gel
filtration data showing MpGlpO to be a monomer were inaccurate, this would be the most
favorable interface for a dimer.
MpGlpO as a Member of the DAAO Superfamily
Looking more closely into the structures of MpGlpO, Bp3DME, and SsGlpO,
conserved regions in the amino acid sequence were determined. In order to get a bigger
picture of the relationships between MpGlpO and other proteins, the program DALI44
was used to identify the structures available in the Protein Data Bank (PDB) that were
similar to MpGlpO. The DALI results are ranked by ‘Z-score’ which reports how many
standard deviations a given comparison is above the mean of all comparisons; Z-scores
below 6 are considered uninteresting. Consistent with the original sequence-based
identification of Bp3DME as the known structure most similar to MpGlpO, the DALI
server reported Bp3DME as the most structurally similar protein (Z-score = 44.0).
SsGlpO was also identified by DALI as being similar, but was actually 171st on the list
(Z-score = 27.8). A more closely similar glycerol-phosphate dehydrogenase (pdb code
2QCU) is from the organism Escherichia coli (EcGlpD) and was 75th on the DALI list
(Z-score = 35.6). Surprisingly, much more similar than these GlpO structures were the
52
glycine oxidases, for which Z-scores ranged from 41.1 to 41.9. These enzymes made up
the top 5 most similar structures along with Bp3DME. We selected the highest resolution
structure, 1RYI from Bacillus subtilis (BsGlyOx), from the top class of glycine oxidases
to compare with MpGlpO. Sarcosine oxidases were also very structurally similar and
only after a large number of these enzymes did any of the glycerol 3-phosphate
dehydrogenases like EcGlpD appear on the list. Glycine oxidases, sarcosine oxidases,
and D-amino acid oxidases (DAAO), all make up the DAAO family of flavoproteins. It is
interesting that MpGlpO is structurally more similar to more functionally distinct
members of the DAAO family than to any of the glycerol 3-phosphate dehydrogenases or
oxidases with which it shares a similar function.
An alignment of the amino acid sequences of EcGlpD and BsGlyOx along with
MpGlpO, Bp3DME, and SsGlpO is presented in Fig. 13. Of particular importance are the
secondary structure markers above each row of five aligned sequences. All of the
structures align well with regard to these main secondary structural elements and they are
labeled with designators (such as α2 and 2) that indicate a consensus secondary
structure, the first description of such for glycerol phosphate dehydrogenases and
oxidases enzymes. One of the functions of the sequence alignment is that it shows which
residues are conserved and where they are found. As can be seen in Fig. 13, the regions
that have the most conservation of sequence tend to be in the active site and in certain
secondary structures. Fully conserved (i.e. identical) residues among the five aligned
structures can be found surrounding and within α-helix 1 (α1), -strand 2 ( 2), the first
310-helix, 14, and 17. α1 packs against the pyrophosphate of the FAD. 2 is right next
to and interacting with the adenine of FAD. The 310 helix is unique in that it contains a
53
Fig. 13: Structure-based Sequence Alignment of MpGlpO and Similar Enzymes.
Aligned sequences are shown for MpGlpO with a similar GlpO, GlpD, glycine oxidase,
and Bp3DME structures with secondary structure types highlighted as red: -strand,
yellow: α-helix, cyan: 310-helix, and magenta: PII-spiral. Fully conserved (i.e. identical)
residues are indicated by asterisks and bold lettering. Every tenth residue of MpGlpO is
indicated by a dot above the sequence.
54
55
conserved threonine along with a slightly less conserved region consisting of threonine,
serine, and histidine. Three residues C-terminal to His44 is a serine that is particularly
important in the active site. This serine (Ser47) interacts directly with the N5 of the flavin
in FAD. Interestingly, as can be seen in Fig. 13, this serine is not one of the conserved
residues across these five structures. SsGlpO has a threonine in that position and
BsGlyOx has an alanine residue. Since serine and threonine have similar properties, this
result is consistent with the expectation that the two GlpO should have active site groups
that are more similar to each other than to glycine oxidases.
Active Site Characterization
Besides the area around the FAD, another feature we looked at was in the FAD
itself. In comparing these structures, we discovered that overlays of the structures based
on the whole protein chain did not provide optimal alignments of the active site regions.
To solve this problem, we used the program hcore (PAKarplus unpublished) which
allowed us to define specific chain regions surrounding the FAD and use those segments
to guide the overlays. This allowed us to better visualize the true differences among these
structures in the active site region (Figure 14). Once aligned with hcore, it was easy to see
that there were no substantial differences in the ways in which the FAD was arranged in
the proteins. Small deviations in one direction correlated with a similar deviation in the
same direction of the entire main chain.
More substantial was the difference in the flavin, or the three-ringed structure of
the FAD. The classic model of the flavin is planar. However, it was readily apparent from
the electron density of MpGlpO that there was a kink or a bend across the three rings. As
56
Fig. 14: Overlay of the Active Sites of MpGlpO, SsGlpO, and Bp3DME. The general
active site of MpGlpO (magenta) is similar to that of Bp3DME (cyan). The active site of
SsGlpO (green) appears to have a deletion in a loop above the flavin, shown with an
arrow.
57
Fig. 15: Comparison of FAD Bending. Both models of GlpO have non-planar flavins
(the 3-ring structure of FAD). The flavin directly interacts in substrate binding and
transfers electrons to produce the product. MpGlpO has a downward twist on one side
and an almost planar ring on the other. The flavin of Bp3DME has a similar twist to
MpGlpO while the flavin of SsGlpO is a downward butterfly bend.
MpGlpO
SsGlpO
Bp3DME
58
seen in Figure 15, there is an asymmetric twist in which the two carbonyl groups lean
towards the viewer and the methyl groups are relatively planar. An almost identical twist
occurs in Bp3DME, but in this case, the dimethyl end also has a small twist. The flavin of
SsGlpO has a so called butterfly bend in which there is symmetrical deviation from
planarity on both sides of the bend at the median of the middle ring.30 While
characteristic of reduced flavins, it is only occasionally observed in oxidized flavins. In
SsGlpO, this butterfly bending directs the reactive nitrogen of the flavin towards the Glp
substrate.30 EcGlpD and BsGlyOx (not shown) have similar twists to each other, with an
almost planar flavin on the carbonyl side and a slight twist on the dimethyl side.
In all structures except Bp3DME, a loop of main chain resides beneath the FAD
(on the si face of the flavin) and can be seen in Fig. 14. This contains the active site
serine, threonine, or alanine residues mentioned above. After necessary refinements, one
of the chains of Bp3DME had the loop beneath the flavin in a similar location as the other
structures while the other chain’s loop was much farther out of the reach of the N5 of the
flavin. Above the flavin (on the re side), there is a loop of amino acids believed to be
important in stabilizing the substrate.30 Interestingly, these loops extend much farther in
MpGlpO, BsGlyOx, and Bp3DME than in SsGlpO and EcGlpD. It looks like an insertion
event has occurred in an area very close to the active sites. These active site differences
are interesting, particularly for MpGlpO, SsGlpO, and Bp3DME because we expected to
see the first two showing the most similarities than either to Bp3DME. A stereo overlay
of these three structures is shown in Fig. 16.
59
Fig. 16: Stereo Overlay of MpGlpO, SsGlpO, and Bp3DME. MpGlpO (magenta),
SsGlpO (green), and Bp3DME (blue) have roughly the same overall structure. FADs and
many alpha helices roughly align. Behind the FAD are additional alpha helices that make
the C-terminal domain. The FAD binding pocket is also roughly conserved.
60
The Reduced MpGlpO Structure
After solving the structure of oxidized MpGlpO, the structure of the chemically
reduced enzyme was determined to visualize how the structure changes when the FAD is
reduced to FADH2. This was performed by soaking the crystals in the reductant sodium
dithionite. Within seconds of placing the crystals in the dithionite solution the crystals
changed from a bright yellow to a pale blue color. This color change indicated a
reduction of the FAD. Data were collected from several reduced crystals and the data
were combined to yield a preliminary reduced MpGlpO (MpGlpOred) structure that could
refined at 2.5 Å resolution (Table 1). Surprisingly, the results show that there appear to be
no significant differences between the oxidized and reduced structures (Figure 17).
Conclusions and Outlook
Through this thesis project, I have determined the structure of MpGlpO and
compared it to other enzymes. This is an important contribution to the scientific
knowledge-base and could be utilized in the design of a new drug against walking
pneumonia. In addition, I have learned far more about scientific research than I expected
to. I have learned about the interesting process of X-ray crystallography, getting to grow
a tiny crystal and find out its structural mysteries. I learned that scientific research has
many detours and ‘dead-ends’. These were never truly ends, because with persistence and
teamwork, one can get back on track and solve a structure.
The work presented here is not complete. There are many questions left
unanswered. One of the interesting questions worthy of further exploration is the
evolutionary relationship of GlpO enzymes from various organisms and how there came
61
Fig. 17: Reduced GlpO Difference Map. There are no significant differences between
the electron densities of reduced MpGlpO (dark blue) and oxidized MpGlpO (light blue).
Positive difference electron density is shown in green and negative difference electron
density is shown in red. Darker greens and reds are from reduced data and lighter greens
and reds are from oxidized data. Both sets roughly align with no large changes to the
model when MpGlpO was reduced.
62
to be such distinct versions. It would also be beneficial to bind ligands into the active site
and observe any structural changes. While my work on MpGlpO may be complete, I
know that I take the lessons I have learned and hope that others will continue to unravel
its mystery.
63
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